Four Classifiers Used in Data Mining and Knowledge Discovery for Petroleum Exploration and Development
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The application of data mining and knowledge discovery in databases for petroleum exploration and development (PED but Case 2 shows that the results of only SVM have zero residuals, while BAYD, BPNN and MRA have unallowable residuals. The reasons are: a) since the two cases are nonlinear problems, the linear MRA is not applicable; b) since the nonlinearity of Case 1 is weak, the nonlinear BAYD, BPNN and SVM are applicable; and c) since the nonlinearity of Case 2 is strong, only nonlinear SVM is applicable. Therefore, it is proposed that: we can adopt MRA when a problem is linear; adopt BAYD, BPNN, or SVM when a problem is weakly nonlinear; and adopt only SVM when a problem is strongly nonlinear. In addition, the predictions of the applicable classifiers coincide with real exploration results, and a commercial gas trap was discovered after the forecast in Case 1 and SVM can correct some erroneous well-log interpretations in Case 2. Key words: Multiple regression analysis; Bayesian discrimination; Back-propagation neural network; Support vector machine; Trap quality evaluation; Oil identification
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it